# TimeAwarePC: A Python Package for Finding Causal Connectivity from Time Series Data [](https://pypi.python.org/pypi/timeawarepc) [](https://timeawarepc.readthedocs.io/en/latest/?badge=latest)
**TimeAwarePC** is a Python package that implements the Time-Aware PC Algorithm for finding the **Causal Functional Connectivity** from time series data, based on recent research in directed probabilistic graphical modeling with time series [[1]](#Citation). The package also includes implementations of Granger Causality and the PC algorithm.
<div align="center">
<a href="https://github.com/shlizee/TimeAwarePC"><img src="https://github.com/shlizee/TimeAwarePC/blob/main/imgs/Schematic-msgs.png?raw=true" width="500px" height="250px"></a>
</div>
## Installation
You can get the latest version of TimeAwarePC as follows.
```
$ pip install timeawarepc
```
## Requirements
- Python >=3.6
- Python packages automatically checked and installed as part of the setup. To use Granger Causality, additional dependency of ```nitime``` which can be installed by ```pip install nitime```.
- R >=4.0
- R package ```kpcalg``` and its dependencies. They can be installed in R or RStudio as follows:
```
> install.packages("BiocManager")
> BiocManager::install("graph")
> BiocManager::install("RBGL")
> install.packages("pcalg")
> install.packages("kpcalg")
```
<!-- - In addition, if you like to use Granger Causality functions in this package, please separately install nitime as follows:
```
pip install nitime
``` -->
## Documentation
[Documentation is available at readthedocs.org](https://timeawarepc.readthedocs.io/en/latest/)
## Tutorial
See the [Quick Start Guide](https://timeawarepc.readthedocs.io/en/latest/gettingstarted.html) for a quick tutorial of the main functionalities of this library and check if it is installed properly.
<!--
## Documentation
[Documentation is available at readthedocs.org](https://timeaware-pc.readthedocs.io/en/latest/) -->
## Contributing
Your help is absolutely welcome! Please do reach out or create a feature branch!
## Citation
Biswas, R., & Shlizerman, E. (2022). Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC Algorithm. https://arxiv.org/abs/2204.04845
Biswas, R., & Shlizerman, E. (2021). Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study. Frontiers in Systems Neuroscience. https://doi.org/10.3389/fnsys.2022.817962
## References
R Clay Reid. (2012) From functional architecture to functional connectomics. Neuron, 75(2):209–217.
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., ... & Woolrich, M. W. (2011). Network modelling methods for FMRI. Neuroimage, 54(2), 875-891.
Judea Pearl. (2009) Causality. Cambridge University press.
Markus Kalisch and Peter Bhlmann. (2007) Estimating high-dimensional directed acyclic graphs with the pc-algorithm. In The Journal of Machine Learning Research, Vol. 8, pp. 613-636.
Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman. (2000) Causation, prediction, and search. MIT press.
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